The present invention relates to a method and apparatus for measuring the breathing rate of a subject, such as a human or animal, and in particular to a way of combining measurements from two or more breathing rate sensors in order to provide an improved measurement of breathing rate.
There is no clinically acceptable method for the non-invasive measurement of breathing rate. A nasal thermistor provides a simple and inexpensive means of tracking respiration but it is obtrusive and is not deemed to be acceptable for patients on the general ward or even the Coronary Care Unit (CCU). Electrical impedance plethysmography (also known as impedance pneumography) can be used to provide an indirect measure of respiration by measuring the changes in electrical impedance across the chest with breathing. In this method a small-amplitude, high-frequency current is injected into the body through a pair of surface electrodes and the resulting voltage is demodulated to obtain impedance measurements. The electrical impedance increases as high-resistivity air enters the lungs during inspiration but part of the change is also due to the movement of the electrodes on the chest wall. When monitored in a clinical environment, the impedance plethysmography (IP) signal is often very noisy and is seriously disrupted by patient movement or change in posture. As a consequence, it has not been considered reliable enough to provide respiration information for regular use on the ward.
Respiratory information is found in other signals recorded from patients with non-invasive sensors. For example, both the electrocardiogram (EGG) and photoplethysmogram (PPG) waveforms are modulated by the patient's breathing. The PPG signal represents the variation in light absorption across a finger or earlobe with every heart beat. This signal is measured at two wavelengths (usually in the red and near infra-red parts of the spectrum) in a standard pulse oximeter.
FIG. 2 shows 30-second sections of IP (FIG. 2A) and PPG (FIG. 2B) signals recorded from a patient in CCU. Referring first to the IP signal in FIG. 2A, the main peaks I, each corresponding to a breath, can be identified. However, changes in electrical impedance with the heart beat (as opposed to respiration) are apparent between t=1607 and t=1608 (marked as “A”) and probably conceal a peak caused by a breath. Referring to FIG. 2B, the modulation of the PPG caused by respiration (marked “I”) can be seen, although there are small movement artefacts B, C at t=1617 and t=1629. FIG. 3 shows the IP and PPG signals from the same patient, for a five-minute period. The effect, marked D, E, of movement artefact on the finger probe from which the PPG signal is recorded becomes very obvious at t=1770 and t=1920.
Clearly the artefacts caused by movement and heartbeat would affect the measurement of breathing rate from these signals. One might consider removing these artefacts by some type of filtering and thresholding.
FIG. 4B shows the effect of applying a Finite Impulse Response (FIR) low-pass filter with a pass-band cut-off at 0.33 Hz (corresponding to 20 breaths per minute) and a stop-band cut-off of 1 Hz (−50 dB) to the IP waveform of FIG. 4A. The cardiac-synchronous changes are filtered out (see the output of the filter between t=1605 and t=1610) and the respiratory cycle is clear. There are as many peaks (i.e. breaths) in FIG. 4B as there are in FIG. 4D, which shows the result of tracking the peaks of the PPG signal shown in FIG. 4C and interpolating (with straight lines) between each peak. The modulation envelope picked out by the peak tracking produces another respiratory waveform. The same information is again displayed on a five-minute timescale in FIG. 5. The breathing rate can be estimated by calculating the interval between two successive peaks of these waveforms, inverting the result and multiplying it by a factor of 60 in order to obtain an estimate of breathing rate in breaths per minute.
The results of these computations over the five-minute period are shown in FIGS. 5E and 5F respectively. The breathing rate is approximately 18 breaths/minute throughout the period, but the occurrence of peaks caused by cardiac as opposed to respiratory changes in the IP signal of FIGS. 5A and 5B gives rise to erroneously high breathing rates at t=1720, 1730, 1780, 1795, 1830 and 1860. Unfortunately no amount of optimisation of the FIR low-pass filter characteristics will ever completely remove the cardiac-synchronous information which is occasionally very prominent. For instance, in a hyperventilating patient, the breathing rate may be as high as 40 breaths per minute, which is similar to a slow heart rate. So separating signal from noise on a fixed basis is impossible.
Similarly, the two major instances D, E of movement artefact in the PPG signal of FIGS. 5C and 5D at t=1770 and t=1920 cause erroneously low estimates of breathing rates at D′ and E′ because there is a significant delay between the last valid peak and the first peak after the movement artefact. The estimate of breathing rate from the PPG signal is also more variable because occurrences of even slight movement artefact affect the tracking of the peaks of the PPG signal.
The present invention provides a method and apparatus for improving measurement of breathing rate by combining two measurements of it in a way which allows valid changes in the breathing rate to be distinguished from artefacts. In accordance with the invention two measurements of breathing rate made in different ways are combined with weights based on the amount of “confidence” in the measurement, to give an improved measurement or estimate of the actual breathing rate. The two measurements may, for example, be obtained using impedance pneumography and photoplethysmography, though other signals which include respiratory information (for instance ECG) can also be used instead of either of these signals, or in addition to them.
In more detail the invention provides a method of measuring breathing rate of a subject comprising the steps of: predicting the value of each of two independent measurements of the breathing rate, making two independent measurements of the breathing rate to produce two measured values, calculating the respective differences between the predicted values and the measured values, and combining the two measured values with weights determined by said differences.
The steps of prediction, measurement, calculation and combination may be repeated continuously. The predicted value for each of the independent measurements may be based on the preceding predicted value and the difference between the preceding predicted value and the preceding measurement.
The predicted value for each of the measurements may be calculated using a linear or non-linear predictive model, and the model may be adaptive, adapting in dependence upon the amount of process noise in the measurements.
In the combining of the two measured values, the weight of each value may vary inversely with the square of the difference between the predicted value and the measurement. In one example the two measured values may be combined according to the formula:
  BR  =                    BR        1            ⁢                        σ          2          2                                      σ            1            2                    +                      σ            2            2                                +                  BR        2            ⁢                        σ          1          2                                      σ            1            2                    +                      σ            2            2                              where BR1 and BR2 are the two measured values of breathing rate and σ1 and σ2 are the differences between the two measured values and their respective predicted values.
The predicted values for the respective measurements may be based on respective models of the system, and the models may include estimates for process noise and sensor noise. The respective models may be mutually independent and may include the same estimates for process noise and sensor noise. The models may be Kalman filters.
Measurements for which the differences between both measured values and their predicted values exceed a predetermined threshold may be discarded. Further, artefacts, for instance caused by movement or heartbeat in the measurements may be identified based on the values of the differences between the measured values and their predicted values. This identification may be used to discard sections of the signal.
Thus with the present invention a prediction is made for each breathing rate measurement and the actual measurement is compared with its prediction. The difference is computed, which is termed the “innovation”, and this innovation is used to calculate a weight which will be given to that measurement when it is combined with the other measurement, also weighted according to its innovation. The weights are calculated so that if the innovation on one measurement channel is high, whereas the innovation on the other measurement channel is low, the measurement from the low innovation channel is more heavily weighted. This is because a high level of innovation from one channel coinciding with a low innovation on the other channel is regarded as indicative of an artefact on the higher innovation channel.